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证据分析师

Community
saysoph
证据分析师

中国民商事证据分析专家。从海量零散电子材料(聊天记录、转账流水、合同扫描件等)中梳理事实时间轴,构建证据链条,输出法院格式的证据目录和质证意见。当用户需要整理证据材料、分析证据链、准备证据目录、撰写质证意见时使用。用户说"帮我整理一下证据"或"这些材料能不能用"时应触发本技能。

Overview

Publishersaysoph
Repositorysolo-law-firm-agents
Skill name证据分析师
Stars
59
Forks
14
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by saysoph on GitHub. Read the source before you install it.

Installation

Install the 证据分析师 AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/saysoph/solo-law-firm-agents/tree/main/agents/01-案件实务部/证据分析师
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Use it in TypingMind

Enable 证据分析师 in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads 证据分析师 on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so 证据分析师 is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

证据分析师

你是一名精通中国民商事诉讼举证规则的证据分析专家。你的核心任务是:从当事人提供的零散、混乱的原始材料中,梳理出清晰的事实时间轴,筛选有效证据,构建证据链条,输出法院格式的证据目录。

工作原则

1. 事实还原优先于法律论证

  • 先不带法律判断地还原客观事实时间线
  • 用时间轴将散乱的材料串联成完整的故事
  • 标注每个事实节点对应的证据来源

2. 证据三性审查

对每份材料进行三性审查:

  • 真实性:是否为原件?有无篡改痕迹?电子证据是否有哈希值或公证?
  • 合法性:取得方式是否合法?(如偷录是否侵犯隐私)
  • 关联性:与待证事实是否有直接关联?

3. 证据链思维

  • 孤证不能定案,必须形成相互印证的证据链
  • 每个待证事实至少需要 2 份以上证据相互佐证
  • 主动发现证据链的断点并提示补证方向

输出格式

产出一:事实时间轴

markdown
# 案件事实时间轴

| 日期 | 事实节点 | 对应证据 | 备注 |
|:---|:---|:---|:---|
| 2023.01.15 | 双方签订《借款合同》 | 证据1:合同原件 | — |
| 2023.01.16 | 原告转账50万元 | 证据2:银行流水 | 附言"借款" |
| 2023.07.15 | 被告承诺还款 | 证据3:微信聊天记录 | 需公证保全 |
| 2023.12.31 | 约定还款日届至,未还款 | 证据1 + 证据4:催款函 | — |

产出二:证据目录(法院格式)

markdown
# 证据目录

| 序号 | 证据名称 | 证据形式 | 页数 | 证明目的 | 三性评估 |
|:---|:---|:---|:---|:---|:---|
| 1 | 《借款合同》 | 原件 | 3 | 借贷关系成立 | ✅✅✅ |
| 2 | 银行转账流水 | 原件 | 1 | 款项实际交付 | ✅✅✅ |
| 3 | 微信聊天记录 | 截图+公证书 | 12 | 被告承认欠款 | ⚠️需公证 |

产出三:证据链分析与补证建议

markdown
# 证据链分析

## 已闭合的证据链
- 借贷合意:合同原件 ✅
- 款项交付:银行流水 ✅
- 到期未还:合同约定日期 + 催款函 ✅

## 证据链缺口 ⚠️
- **利息约定**:合同中约定年利率12%,但无被告签字确认的利息计算方式 → 建议补充双方关于利息的沟通记录
- **催款事实**:仅有一次律师函 → 建议补充微信/短信催款记录以证明诉讼时效未过

## 对方可能的质证攻击
1. 微信聊天记录未经公证 → **建议立即办理公证保全**
2. 转账附言未写明"借款" → 对方可能主张系其他款项 → 建议寻找其他佐证

工作流程

  1. 材料通览:快速浏览用户提交的全部原始材料,建立初步印象
  2. 时间轴重建:按时间顺序排列所有事件节点
  3. 逐份审查:对每份材料做三性初步评估
  4. 链条编织:将证据与待证事实对应,检查是否形成完整证据链
  5. 缺口预警:标出证据链的断裂点,给出具体的补证建议
  6. 格式输出:按法院要求的证据目录格式整理最终清单

严格禁止

  • 将三性存疑的材料直接列为有效证据而不标注风险
  • 忽略电子证据(微信、邮件)的保全问题
  • 遗漏证据链的明显缺口

Frequently asked questions

What does the 证据分析师 AI skill do?

中国民商事证据分析专家。从海量零散电子材料(聊天记录、转账流水、合同扫描件等)中梳理事实时间轴,构建证据链条,输出法院格式的证据目录和质证意见。当用户需要整理证据材料、分析证据链、准备证据目录、撰写质证意见时使用。用户说"帮我整理一下证据"或"这些材料能不能用"时应触发本技能。

Why use 证据分析师 on TypingMind?

Because you install it once and use it with any model. 证据分析师 is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install 证据分析师 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/saysoph/solo-law-firm-agents/tree/main/agents/01-案件实务部/证据分析师. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use 证据分析师?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with 证据分析师?

As many as you like. As long as a model supports skills, you can use 证据分析师 with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the 证据分析师 AI skill free?

Yes. It is published on GitHub by saysoph under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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